Transitioning IIoT Data Processing: An Experience with InfluxDB on Kubernetes

With the rapidly growing field of the Industrial Internet of Things (IIoT), the management of tremendous amounts of high-frequent real-time data becomes critical. Moving from the traditional static or cloud-based model toward a more dynamic containerized framework, either self-hosted or through Amazon EKS (Elastic Kubernetes Service), is essential to exploit scalable and robust deployment capability. However, the principal challenges with the transition of IIoT time-series microservice to Kubernetes include effective latency, throughput management, and system compatibility issues.The aim of this research is to overcome the above challenges and to transition the IIoT time-series microservice to Kubernetes efficiently by leveraging the functionalities of InfluxDB for efficient storage, retrieval, and real-time analytics of data. It also includes the complexities and possible barriers that they went through while moving from cloud-based IIoTaaS platforms into self-hosted systems or Amazon EKS. The study is conducted through a case study approach, which involves analyzing the transition process and conducting practical experiments. The results provide insights into the challenges and considerations in transitioning to In-fluxDB within a Kubernetes architecture. The study also provides a set of techniques and procedural guidelines that can help businesses in transitioning their IIoT data processing systems to Kubernetes while leveraging the capabilities of InfluxDB.